MixORNet: Encoding Scent Mixtures via Combinatorial Protein Activations for Olfactory Perception

Perceptual modeling of complex scent mixtures remains largely underexplored in computational olfaction. Existing approaches share two critical limitations: (1) they rely solely on the chemical properties of constituent odourants, neglecting selective olfactory receptor (OR) binding, and (2) they model perception via pairwise molecular attention, ignoring the fact that humans perceive mixtures holistically. We propose MixORNet, the first framework to encode scent mixture perception at the receptor level. MixORNet represents the mixture as a fully connected graph and uses a graph attention network (GAT) to extract holistic mixture-level features. These features further interact with a panel of $N$ OR proteins via cross-attention, yielding $N$-dimensional OR-activation fingerprints, with each dimension denoting the activation probability of each receptor. MixORNet achieves state-of-the-art performance on perceptual distance prediction and multilabel olfactory annotation of scent mixtures. Moreover, it can reproduce the olfactory white phenomenon, aligning with established psychophysics. Notably, ablation of the OR panel size reveals that the optimal $N$ is substantially smaller than the approximately 400 human ORs in specific scenarios, providing the first AI-derived estimate for the intrinsic dimension of the human olfactory perceptual subspace. The main code is available in the anonymous repository https://anonymous.4open.science/r/MixORNet/